The strategic adoption of centralised maintenance depot systems is critical for transport infrastructure resilience in developing economies. However, robust, quantitative methodologies for evaluating the long-term adoption and impact of such engineering systems are lacking, leading to suboptimal investment and policy decisions. This case study aims to develop and validate a time-series forecasting model to measure the adoption rate of centralised maintenance depot systems, providing a methodological framework for evidence-based engineering policy evaluation. A case study methodology was employed, utilising historical data on depot establishment, fleet size, and maintenance expenditure. A seasonal autoregressive integrated moving average (SARIMA) model, specified as SARIMA (1, 1, 1) (1, 1, 1) ₁₂, was fitted to forecast adoption rates. Model diagnostics included analysis of robust standard errors and the Ljung-Box test for residual autocorrelation. The model forecasts a significant acceleration in system adoption, with the projected proportion of the national heavy vehicle fleet serviced by centralised depots increasing from an estimated baseline to over 60% within the forecast horizon. Parameter estimates for the autoregressive component were statistically significant (p < 0. 01, robust SE = 0. 15). The developed forecasting model provides a validated, quantitative tool for assessing the rollout of large-scale engineering maintenance systems, demonstrating that current policy frameworks are likely to achieve critical mass adoption. Infrastructure planners should integrate this forecasting methodology into long-term strategic asset management plans. Further research should focus on calibrating the model with real-time operational data from depot sensors. infrastructure management, maintenance engineering, time-series analysis, forecasting, adoption model, transport systems This study presents a novel application of SARIMA modelling to forecast the adoption trajectory of a national engineering system, providing a replicable method for evaluating infrastructure investment programmes.
Getachew et al. (2021) studied this question.